Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.
Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.
The growth in population and progression of internet services, data size is getting increased day by day where 105000s of Trillion of data files are there in cloud available in unstructured nature. The coming times of Big Data are rapidly arriving for just about all industries. The Big Data can help in metamorphose major business processes by advisable and correct analysis of accessible data. Big data have also played an essential role in crime discover. Hadoop is opensource software in the form of an extremely scalable and fault tolerant distributed system which plays a very remarkable role in data storage and its processing. The Apache Hadoop Yarn is an open source framework developed by Apache Software Foundation. It is used for nursing Big Data. It endows storage as well as processing functionality. In this paper, we aimed to demonstrate a close look to about Yarn. The Yarn as a usual computing fabric to support MapReduce and another application instance within of the same kind Hadoop cluster. Yarn allow multiple applications to run simultaneously on the coequal shared cluster and assent applications to negotiate resources based on necessity. In the end, we are in a nutshell discuss about the design, development, and current state of deployment of the next generation of Hadoop's computes platform Yarn.
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